Why is GPU Faster Than CPU?

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Why is the GPU faster than the CPU? The CPU is essential for all computational tasks, GPU on the other hand is ideal for computing machine-learning tasks as well as high-end graphics.

A CPU is made up of transistors, each with multiple cores, necessary for handling primary computational functions of a computer.

The GPU focuses more on parallel processing, since it was developed to assist in 3D rendering.

Therefore, GPUs have much more processing speed than CPUs.

Let us first understand what CPUs and GPUs are then explore why GPU is faster than CPU.

What is a CPU?

Your computer’s Central Processing Unit is its core processor, which means that it is the unit responsible for computing every single action that your computer performs.

Your CPU accepts commands from the user i.e. you, translates it to binary code and processes it using its logical structure.

It then communicates with other components informing them of their respective tasks and an order in which they should be performed.

It performs all of these actions within milliseconds regardless of their complexity.

Your CPU is made up of several essential components including:

  • CPU clock and control unit– A CPU’s clock measures its processing speed in Hertz. Your CPU’s clock generator produces pulses making other CPU components run in sync to complete a process. Processors with higher clock rate transmit these pulses quickly, thereby, allowing tasks to be processed quickly. Higher clock rates also enable quick completion of processor-intensive tasks.
  • Cache– It is a fast memory that acts as a bridge between processor and main memory. Made up of faster static RAM chips, it allows faster execution of instructions along with reading and writing of data at higher speeds. Since, CPU performs millions of calculations in a second it requires memory to keep up with this speed. Hard disks and RAM (Random Access Memory) cannot keep up with this speed, therefore cache is used in their place. Cache is also arranged in levels to store data according to its priority. L1 cache is fastest and stores data required immediately, whereas L3 cache is slower than L1 and L2, and stores lowest priority data in cache.
  • Cores– A core is your CPU’s main processing unit. Previously, CPUs were single core systems, ensuring their focus on completing one task before starting another. Now, even general use computers are equipped with four to eight cores. This allows CPUs to multitask, as one core completes one process and simultaneously another core is working on another process. With multithreading/hyperthreading, a single core can be further divided into two virtual cores, thereby significantly increasing computing speed and power.
  • MMU (Memory Management Unit)– This physical unit which deals with caching and virtual memory operations, might be located within a CPU or in an IC (Integrated Chip). It is your CPU’s MMU which receives requests for data and then searches for where that particular data is located in ROM (read-only memory) or RAM (random access memory).
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These aforementioned components work in sync as a unit to accomplish computation of several tasks parallelly.

Your computer is able to perform multiple tasks such as connecting to the internet as well as displaying a file simultaneously.

It is accomplished by CPU cores rapidly switching between thousands of different tasks every second.

What is a GPU?

CPUs are often unable to process high-end graphics, as this process is fairly complex and resource intensive.

In order to address this issue, a hardware component was developed, which is now used for other complex applications including artificial intelligence and machine learning.

Processing graphics, be it low-end or high-end requires a processor to compute complex mathematics.

Moreover, these complex mathematical processes have to be solved parallelly in order to produce desired output.

Since CPUs are unable to handle this level of problem solving even for low-end graphics, using them for this purpose results in glitchy graphics and CPU wear and tear.

For example, a graphics-intensive video game has to display thousands of geometrical patterns on the screen at every moment.

Not only are there plenty of geometrical shapes, each one also has its own set of characteristics like color and movement. CPUs are not equipped to compute such a large number of tasks parallely, therefore, GPUs are required.

GPUs are quite similar to CPUs as they are also made up of memory, cores and other components.

A GPU, also known as, Graphics card, focuses on parallel processing of data as it is equipped with several thousands of cores.

Additionally, it is able to process tasks and instructions of various different programs without increasing pressure on its resources and speed.

However, a faulty graphics card is not optimal for management of multiple tasks at once.

A single GPU core is fairly weak when compared to a single CPU core. Additionally, GPUs do not operate well with varying computer systems.

You need to configure your GPU to work properly with your system or it will not produce optimum results.

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Their main focus is on processing huge amounts of data parallely in batches.

A GPU processes data in batches to generate a clear and glitch-free display by processing data in huge volumes.

8 Reasons Why the GPU Faster Than the CPU?

Why is the GPU Faster Than the CPU

GPUs are significantly faster than CPUs as they both follow instructions given to them in different ways.

The CPU focuses on sequential processing and therefore all its cores complete processing tasks one by one.

Whereas, the GPU was designed for multitasking without reduction in processing speed.

Even after hyperthreading CPUs are unable to match GPUs processing speeds, because GPUs do not have a limited number of cores.

Regardless of each individual core’s strength, when an entire graphics card is assigned a task of processing high-end graphics or other mathematical calculations, it effectively distributes its workload through parallel processing.

Therefore, GPU is used for offloading processor intensive tasks.

Other differentiating factors which make GPUs faster than CPUs are:

1. Cores

A CPUs cores are small but powerful. Whereas, GPUs have several thousands of cores even though they are even smaller and weaker in comparison.

However, it is this high number of cores that make GPUs much more powerful.

2. Number of Threads

A CPU is designed in such a way that each of its cores can be split into two visual threads, with each thread capable of functioning individually.

Whereas, a GPU is designed to function even with a single instruction and multiple threads functioning parallelly.

While CPUs have only one thread dedicated to an instruction, GPUs process much faster, as about 32 threads are dedicated to execute a single instruction.

3. Mode of Processing

CPU architecture is designed keeping in mind its main objective which is managing a computer with its limited resources.

It processes data in a sequential order, providing more resources to programs which require them to run properly.

A GPU’s main objective is not to support all processes but focus on processes which require use of its high-end resources.

It was designed, not as a replacement to CPU, but to reduce stress on it by facilitating parallel processing of instructions.

4. Implementation of Threads

GPUs have a more structured implementation of usage of threads when compared to CPUs.

This adds to GPU’s speed and power as CPUs use of threads for implementation of a task does not have any order to it.

Several algorithms for image processing can be implemented parallelly as GPUs have a proper system in place for rotation of threads for every instruction.

This method proves to be ideal as GPU resources, power and speed are used optimally.

Other ways in which GPUs are better than CPUs

5. Memory Bandwidth

Not only are GPUs faster than CPUs owing to their ability to parallelly process tasks, they also have much higher memory bandwidth.

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A CPU and GPU manufactured around the same time differ in their performance ability, with GPU performing 10 times better in terms of memory system bandwidth.

Since, it is significantly higher than CPU’s performance.

Even with software applications that have not been fine-tuned to GPU’s configuration, it is possible to process data almost 100 times faster than CPUs.

This is possible as GPUs offer large caches for data storage which are able to keep up with their speed of processing data through multiple parallel processes.

6. Reduce Load

CPUs are used for optimally operating an entire computer; however, they are unable to reduce load on memory subsystems. GPUs are able to achieve this as the number of registers is dynamically changed.

It goes from less than a hundred to more than 250 as soon as it finds a GPU connected to its system.

7. Simultaneous Execution

A GPU’s ability to parallelly process data is well known, however, GPUs are also able to simultaneously execute completely different tasks, which are not interconnected in any way.

For example, while data is being copied to and from this device asynchronously, it can also decode video, process images for better graphics and perform calculations for neural networks.

None of these processes lag, as GPU provides adequate resources for each process individually, not letting them be affected by other processes being carried out simultaneously.

8. Shared Memory

Similar to CPUs, GPUs also have shared memory or cache.

However, in order to keep up with GPU’s processing speed, this shared memory is significantly faster than CPUs L1 cache.

Algorithms that require much more storage space locally due to their size or speed at which they need to be processed are stored in this shared memory space.

Conclusion

GPUs cannot be used to replace CPUs in a computer structure. Since, they do not have robust architecture like a CPU.

GPUs are currently only used for reducing load on CPUs as high-end resource intensive computing operations are shifted to GPUs for processing.

They are only used to make applications run faster and smoother from a user’s perspective.

Now that you know all the reasons, you no longer need to wonder why GPUs are faster than CPUs.

About Dominic Cooper

Dominic CooperDominic Cooper, a TTU graduate is a computer hardware expert. His only passion is to find out the nitty gritty of all computers. He loves to cook when he is not busy with writing, computer testing and research. He is not very fond of social media. Follow Him at Linkedin